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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from math import *\n",
        "\n",
        "df = pd.read_csv('sample_final.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "o9BFTkghxX8c",
        "outputId": "1b918aea-52d0-4703-e149-14eaddc820a8"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0                            ФИО  Место рождения  Возраст  \\\n",
              "0           0     Комаров Никифор Германович  клх Муравленко       24   \n",
              "1           1     Станимир Артёмович Антонов    к. Пятигорск       20   \n",
              "2           2  Синклитикия Юльевна Мясникова       г. Шамары       25   \n",
              "3           3   Добромысл Бориславович Мухин       г. Шамары       26   \n",
              "4           4      Клавдия Романовна Ефимова       д. Домбай       18   \n",
              "\n",
              "   День недели         Видеозапись  \n",
              "0      Суббота             Матрица  \n",
              "1        Среда       Семинар по ML  \n",
              "2      Суббота  Видосы про собачек  \n",
              "3      Четверг   Бегущий по лезвию  \n",
              "4  Понедельник        Лекция по ML  "
            ],
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              "      <td>Станимир Артёмович Антонов</td>\n",
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              "      <td>Видосы про собачек</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>Добромысл Бориславович Мухин</td>\n",
              "      <td>г. Шамары</td>\n",
              "      <td>26</td>\n",
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            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rg2fxLTWxTaT",
        "outputId": "31de908d-ad5b-4f9f-b1cb-7f3fa4a06c4b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df.loc[(df['День недели'] == 'Четверг') & (df['Видеозапись'] == 'Видосы про собачек')].shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[(df['День недели'] == 'Четверг')].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cOdKNKAYypgp",
        "outputId": "8b0fbd96-6cb0-4b83-e636-9423458515d6"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "11/100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1lIQCjly6xC",
        "outputId": "b33b0a6c-2932-4a9b-8771-5ae7d5b62054"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.11"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dogs = df.loc[(df['Видеозапись'] == 'Видосы про собачек')]"
      ],
      "metadata": {
        "id": "OrBEbL-_0WU-"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[(df['Видеозапись'] == 'Видосы про собачек')]['Видеозапись'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "beWFNUBC04zr",
        "outputId": "46c851c7-f6c2-467c-d5a3-14576b9e63f6"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Видеозапись\n",
              "Видосы про собачек    61\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for day in ['Суббота', 'Среда', 'Четверг', 'Понедельник', 'Вторник', 'Пятница',\n",
        "       'Воскресенье']:\n",
        "  print(day, df.loc[(df['День недели'] == day) & (df['Видеозапись'] == 'Видосы про собачек')]['Видеозапись'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ynBbTjOS2Z9C",
        "outputId": "479626fa-7a91-4622-e79b-1d7df744a4bc"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Суббота Видеозапись\n",
            "Видосы про собачек    17\n",
            "Name: count, dtype: int64\n",
            "Среда Видеозапись\n",
            "Видосы про собачек    33\n",
            "Name: count, dtype: int64\n",
            "Четверг Видеозапись\n",
            "Видосы про собачек    11\n",
            "Name: count, dtype: int64\n",
            "Понедельник Series([], Name: count, dtype: int64)\n",
            "Вторник Series([], Name: count, dtype: int64)\n",
            "Пятница Series([], Name: count, dtype: int64)\n",
            "Воскресенье Series([], Name: count, dtype: int64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['День недели'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64htuhrH2j5t",
        "outputId": "a63b8d27-fa4e-4125-c8a8-b9cc37f02bdb"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Суббота', 'Среда', 'Четверг', 'Понедельник', 'Вторник', 'Пятница',\n",
              "       'Воскресенье'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n = 100\n",
        "p = 11/100\n",
        "\n",
        "total = 0\n",
        "for k in range(1, 9):\n",
        "  P = comb(n, k) * p ** k * (1 - p) ** (n - k)\n",
        "  total += P\n",
        "  print(k, P)\n",
        "print('Total:', total)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2YpPq11B3wyB",
        "outputId": "a10efb9d-795b-4fad-9f1d-a70343407faa"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 0.00010739976794629897\n",
            "2 0.0006570693668175256\n",
            "3 0.002652886806851284\n",
            "4 0.007951208491321122\n",
            "5 0.018868485768168772\n",
            "6 0.03692428394707185\n",
            "7 0.06128364301969871\n",
            "8 0.08805220057465137\n",
            "Total: 0.21649717774252691\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "zPriQG-f4YaM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "1 - total"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTmt7s7a4X_i",
        "outputId": "96ea2fb6-26aa-48b8-bc73-14cb1009a17a"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7835028222574731"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    }
  ]
}